Image processing system, image processing apparatus and image processing method

ABSTRACT

An image processing system includes an image source apparatus, an image processing apparatus and a target apparatus. The image processing apparatus includes an image capturing unit, a mode determining unit and an image compression unit. The image capturing unit captures an image provided by the image source apparatus and divides it into blocks. The mode determining unit, coupled to the image capturing unit, includes compression modes. The mode determining unit receives a first block of the blocks, and a classification model in the mode determining unit analyzes the first block, and based on the analysis result, selects a first compression mode for the first block from the compression modes. The image compression unit, coupled to the image capturing unit and the mode determining unit, compresses the first block into a first compressed block according to the first compression mode and transmits the first compressed block to the target apparatus.

BACKGROUND OF THE INVENTION Field of the Invention

This invention relates to image processing, in particular to an imageprocessing system, image processing device and image processing method.

Description of Related Art

Generally, in image processing process, three color sampling methodscommonly used in the industry are 4:4:4, 4:2:2 and 4:2:0. 4:4:4 colorsampling achieves whole image transmission without channel compression,and requires a relatively large transmission bandwidth. 4:2:2 colorsampling removes one half of the signals of the second and thirdchannels, and requires ⅔ of the transmission bandwidth of the 4:4:4color sampling method. As for 4:2:0, more blue and red chroma will betaken away, so the occupied transmission bandwidth is even smaller.

For example, if the image currently being processed is a computerdesktop or a simple still image, because the compression ratio for suchimages is usually high, and the bandwidth occupied by the transmissionis relatively small, even if uncompressed 4:4:4 color sampling method isused, the occupied bandwidth is still within an acceptable range, andwill not produce distortion after image compression. Conversely, if theimage currently being processed is a complex image, a compressed 4:2:2or 4:2:0 color sampling method are often used so as to not exceed thelimit of the total transmission bandwidth.

However, since it is difficult to know or predict in advance whether theimage provided by the user is a simple image or a complex image, currentimage processing systems are not able to automatically select the mostsuitable color sampling method based on the complexity of the imagecurrent being process, and therefore cannot both meet the bandwidthlimitation and avoid image distortion at the same time.

SUMMARY

In view of this, the present invention provides an image processingsystem, image processing device and image processing method whichobviates the problems encountered in the conventional art.

An embodiment of the present invention provides an image processingdevice. In this embodiment, the image processing device includes animage capturing unit, a mode determining unit, and an image compressionunit. The image capturing unit captures the image and divides the imageinto a plurality of blocks. The mode determining unit is coupled to theimage capturing unit. The mode determining unit includes a plurality ofcompression modes. The mode determining unit receives a first blockamong the blocks, analyzes the first block by a classification model ofthe mode determining unit, and based on the analysis result, selects afirst compression mode for the first block from the multiple compressionmodes. The image compression unit is coupled to the image capturing unitand the mode determining unit. The image compression unit compresses thefirst block according to the first compression mode to generate a firstcompressed block.

In one embodiment, the classification model is an artificialintelligence (AI) model obtained through a training mechanism. Thetraining of the classification model uses training data that includes aplurality of sample images, preferably labeled by complexity.

In one embodiment, the mode determining unit inputs the first block tothe classification model, and selects the first compression mode for thefirst block from the multiple compression modes based on the complexityresult output by the classification model.

In one embodiment, the first compressed block further includescompression information corresponding to the first compression mode.

In one embodiment, the mode determining unit obtains a second block fromthe blocks, analyzes the second block by the classification model, andbased on the analysis result, selects a second compression mode for thesecond block from the multiple compression modes. The image compressionunit compresses the second block according to the second compressionmode to generate the second compressed block. The second block isdifferent from the first block.

In one embodiment, the first compression mode and the second compressionmode are different and use different color sampling methods.

Another embodiment of the present invention provides an image processingsystem. In this embodiment, the image processing system includes animage source device, an image processing device, and a target device.The image source device provides images. The image processing device iscoupled to the image source device. The image processing device includesan image capturing unit, a mode determining unit, and an imagecompression unit. The image capturing unit receives the image from theimage source device and divides the image into a plurality of blocks.The mode determining unit is coupled to the image capturing unit. Themode determining unit includes a plurality of compression modes. Themode determining unit receives a first block among the blocks, analyzesthe first block by a classification model of the mode determining unit,and based on the analysis result of the classification model, selects afirst compression mode for the first block from the multiple compressionmodes. The image compression unit is coupled to the image capturing unitand the mode determining unit. The image compression unit compresses thefirst block according to the first compression mode to generate a firstcompressed block. The target device is coupled to the image compressionunit of the image processing device and receives the first compressedblock. The target device selects a first decompression modecorresponding to the first compression mode based on the compressioninformation, and decompresses the first compressed block according tothe first decompression mode.

Another embodiment of the present invention provides an image processingmethod. In this embodiment, the image processing method includes thefollowing steps: capturing an image; dividing the image into a pluralityof blocks; selecting a first block among the blocks; analyzing the firstblock; based on the analysis result, selecting a first compression modefor the first block from a plurality of compression modes; andcompressing the first block according to the first compression mode togenerate a first compressed block.

Compared with the conventional art, the image processing system, imageprocessing device, and image processing method according to embodimentsof the present invention can use a classification model (such as anartificial neural network obtained through deep learning) toautomatically determine the complexity of an image block (such ascomplex image, medium image, or simple image), and can further select acompression mode that is most suitable for the image block based on thedetermined complexity. Therefore, the system, device and method can notonly meet the bandwidth limitation, but also avoid image distortionperceived by human eyes.

The advantages and spirit of the present invention can be furtherunderstood through the following detailed description of the inventionand the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings are described as follows:

FIG. 1 is a functional block diagram of an image processing systemaccording to a preferred embodiment of the present invention.

FIG. 2 is a schematic diagram showing a classification model of theembodiment.

FIGS. 3A and 3B are schematic diagrams respectively showing that theclassification model receives different blocks of the image to beprocessed and correspondingly outputs the complexity results of thedifferent blocks.

FIG. 4 is a flowchart illustrating an image processing method accordingto another preferred embodiment of the present invention.

FIG. 5 is a flow chart illustrating that step S16 in FIG. 4 may furtherinclude steps S160-S164.

FIG. 6 is a flowchart illustrating that steps S20 to S24 can be furtherperformed after the image processing method completes step S18 in FIG.4.

FIG. 7 is a flowchart illustrating that steps S30 to S40 can be furtherperformed after the image processing method completes step S18 in FIG.4.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Reference will now be made in detail to exemplary embodiments of thepresent invention, and examples of the exemplary embodiments will bedescribed with the accompanying drawings. Elements/components with thesame or similar reference numerals used in the drawings and embodimentsare used to represent the same or similar parts.

One preferred embodiment of the present invention provides an imageprocessing system. In this embodiment, the image processing system isused to perform image processing procedures on the images to generateprocessed images.

Please refer to FIG. 1, which is a functional block diagram of an imageprocessing system in a preferred embodiment. As shown in FIG. 1, theimage processing system IPS includes an image processing device 1, animage source device 2, and a target device 3. The image processingdevice 1 is coupled between the image source device 2 and the targetdevice 3. The image source device 2 may be, for example, a computer,video player, video signal switch, etc. The target device 3 may be, forexample, a display device such as a monitor, TV, projector, etc. Theimage processing device 1, including each of its components describedbelow, may be implemented by electrical circuitry including logiccircuits such as FPGA, and/or processors or which execute computerexecutable program code stored in computer readable non-volatilememories.

In this embodiment, the image source device 2 provides an image M to theimage processing device 1. The image processing device 1 compresses theimage M provided by the image source device 2 to generate a firstcompressed block B1′ and a second compressed block B2′ and outputs themto the target device 3.

In one embodiment, the image processing device 1 includes an imagecapturing unit 10, a mode determining unit 12, and an image compressionunit 14. The mode determining unit 12 is connected to the imagecapturing unit 10, and the image compression unit 14 is coupled to theimage capturing unit 10 and the mode determining unit 12.

In one embodiment, the mode determining unit 12 includes a plurality ofcompression modes (e.g., it stores information regarding a plurality ofcompression modes). The compression modes include a first compressionmode C1 and a second compression mode C2, but the invention is notlimited to this. In addition, the mode determining unit 12 also includesa classification model CM, the details of which will be described indetail later.

The image capturing unit 10 receives the image M from the image sourcedevice 2 and divides the image M into a plurality of blocks, and theblocks include, without limitation, at least a first block B1 and asecond block B2. The shapes of the first block B1 and the second blockB2 may be the same or different, and the invention is not limitedthereto. The areas of the first block B1 and the second block B2 may beequal or unequal, and the present invention is not limited thereto.

Here, when the mode determining unit 12 receives the first block B1 fromthe image capturing unit 10, the classification model CM analyzes thefirst block B1, and based on the analysis result, selects from themultiple compression modes in the mode determining unit 12 a firstcompression mode C1 for the first block B1, and outputs the firstcompression mode C1 to the image compression unit 14 (e.g. it outputs anindication that the first compression mode is selected).

More specifically, when the mode determining unit 12 receives the firstblock B1, the mode determining unit 12 inputs the first block B1 to theclassification model CM. The classification model CM performs ananalysis operation on the first block B1 to produce a complexity resultof the first block B1. Then, from the multiple compression modes, themode determining unit 12 selects a first compression mode C1 thatcorresponds to the complexity result of the first block B1 (e.g., themode determining unit 12 may store a lookup table that relates eachpossible complexity result to a compression mode).

Next, the image compression unit 14 receives the first block B1 from theimage capturing unit 10 and the first compression mode C1 from the modedetermining unit 12. The image compression unit 14 compresses the firstblock B1 according to the first compression mode C1 to generate a firstcompressed block B1′, and output the first compressed block B1′ to thetarget device 3. (Here, note that the image compression unit 14pre-stores multiple compression algorithms, and will select onealgorithm according to the compression mode indication received from themode determining unit 12.) The first compressed block B1′ may includecompression information corresponding to the first compression mode C1.For example and without limitation, the compression information mayinclude information such as the color sampling method and thecompression ratio adopted by the first compression mode C1.

Upon receiving the first compressed block B1′, the target device 3selects a corresponding first decompression mode D1 according to thecompression information included in the first compressed block B1′, anddecompresses the first compressed block B1′ according to the firstdecompression mode D1 to generate a first restored block B1″. Since thecompression information includes information regarding the firstcompression mode C1, the target device 3 can select, among thedecompression modes, the first decompression mode D1 corresponding tothe first compression mode C1 based on the compression information. Theabove functions of the target device 3 is implemented by electricalcircuitry including logic circuits such as FPGA, and/or processors whichexecute computer executable program code stored in computer readablenon-volatile memories. The target device 3 pre-stores the multipledecompression algorithms.

In an embodiment, the classification model CM may be an artificialneural network obtained through deep learning. For example, duringtraining of the neural network, N sample images (N is a positiveinteger) SI1˜SIN are inputted to the classification model CM beingtrained, so that the classification model CM can continuously adjust itsinternal parameters by analyzing each sample image SI1˜SIN during thetraining process. After training, the trained classification model CMcan be used to analyze new images inputted into it and outputs thecomplexity results of the input images. In the present embodiment, thetraining method is not limited to any particular training method.Referring to FIG. 2, the neural network of the classification model CMhas multiple connected layers of nodes, including an input layer INL,one or more hidden layers HL, and an output layer OL. The input layerINL is coupled to the hidden layer HL and the hidden layer HL is coupledto the output layer OL.

More specifically, when training the classification model CM, the firstsample image SI1 of the N sample images SI1˜SIN is inputted to theclassification model CM. When the input layer INL receives the firstsample image SI1, the input layer INL transmits the first sample imageSI1 to the hidden layer HL. Next, the hidden layer HL uses algorithmssuch as convolution, pooling, or fully connected to process the firstsample image SI1 to generate a complexity result T1 for the first sampleimage SI1, and outputs the complexity result via the output layer OL.The output is fed back to the classification model CM, and theclassification model CM then adjusts the internal parameters of any oneor any combination of the input layer INL, the hidden layer HL, and theoutput layer OL based on the feedback. Preferably, in supervisedtraining, the training samples are associated with training labels whichindicate the correct complexity values for each sample image, and thefeedback is generated based on a comparison of the complexity resultoutputted by the model and the training label of the sample image SI1.It should be noted that because the classification model CM is still inthe process of training, the complexity result T1 output by the outputlayer OL may not be correct, and when the result of the output layer OLis wrong, the feedback is used to adjust the internal parameters of theinput layer INL, the hidden layer HL, and/or the output layer OL toimprove the model.

This process is repeated using labeled sample image SI2 of the N sampleimages SI1˜SIN, by inputting the sample image SI2 to the classificationmodel CM having the previously adjusted internal parameters. Theinternal parameters are further adjusted based on the processing of thesample image SI2 in similar manners as described above.

Similarly, the third sample image SI3 to the Nth sample image SIN arefed to the classification model CM and processed in the same way tofurther adjust the internal parameters of the input layer INL, thehidden layer HL, and/or the output layer OL. The final result is atrained classification model CM, which can be used to process new inputimages that were not used in training to accurately generate thecomplexity result of the new input image.

Next, please refer to FIGS. 3A and 3B. FIGS. 3A and 3B are schematicdiagrams respectively showing that the trained classification model CMreceives different blocks of the image M to be processed andcorrespondingly outputs the complexity results of the different blocks.

As shown in FIG. 3A, when the input layer INL of the trainedclassification model CM receives the first block B1 of the image M, itshidden layer HL operates on the first block B1 using the internalparameters obtained through training, to generate the complexity resultR1 of the block B1 which is output through the output layer OL. Based onthe complexity result R1, the mode determining unit 12 selects a firstcompression mode C1 corresponding to the complexity result R1 from (K+1)compression modes C0 to CK. Here, the first compression mode C1 of thisembodiment is only used as an example for illustration, and actually thenth compression mode Cn is selected according to the first block B1,where n is any integer from 0 to K.

Similarly, as shown in FIG. 3B, when the input layer INL of the trainedclassification model CM receives the second block B2 of the image M(different from the first block B1), its hidden layer HL operates on thesecond block B2 using the internal parameters obtained through training,to generate the complexity result R2 of the second block B2 which isoutput through the output layer OL. Based on the complexity result R2,the mode determining unit 12 selects a second compression mode C2corresponding to the complexity result R2 from the (K+1) compressionmodes C0 to CK. Here, the second compression mode C2 of this embodimentis only used as an example for illustration, and actually the nthcompression mode Cn is selected according to the second block B2, wheren is any integer from 0 to K.

In practical applications, if the complexity result R2 of the secondblock B2 is different from the complexity result R1 of the first blockB1, the second block B2 will be compressed according to the secondcompression mode C2 that is different from the first compression mode C1used to compress the first block B1. On the other hand, if thecomplexity result R2 of the second block B2 is equal to the complexityresult R1 of the first block B1, the second block B2 will be compressedusing the same first compression mode C1 as used for the first block B1.

It should be noted that the complexity result R1 of the first block B1and the complexity result R2 of the second block B2 can be compleximage, medium image, and simple image in terms of complexity, and eachhas its own corresponding compression mode that is suitable for thecomplexity result. The compression mode can be, without limitation,lossy compression or lossless compression. Many lossless compressionmethods and lossy compression methods with different degrees ofcompression are known and their details are not described here.

In addition, in the first compression mode C1 or the second compressionmode C2, a complete color sampling method (e.g. 4:4:4) or a reduced-sizecolor sampling method (e.g. 4:2:2 or 4:2:0) may be used to perform colorsampling, so that the first restored block B1″ and the second restoredblock B2″ obtained by the target device 3 after decompression may be thesame as or different from the respective uncompressed first block B1 anduncompressed second blocks B2 received by the mode determining unit 12.

Taking the first block B1 as an example, if the complexity result R1 ofthe first block B1 output by the classification model CM is “simpleimage”, indicating that the bandwidth occupied by the first block B1during transmission would be relatively small and it is suitable to becompressed with a lossless compression mode, the mode determining unit12 selects lossless compression as the first compression mode C1 for theimage compression unit 14 to compress the first block B1, and to use acomplete color sampling method (e.g. 4:4:4) to perform color sampling.Therefore, the first restored block B1″ obtained after the target device3 performs decompression will be the same as the uncompressed firstblock B1 received by the mode determining unit 12.

Conversely, if the complexity result R1 of the first block B1 output bythe classification model CM is a “complex image”, indicating that thebandwidth occupied by the first block B1 during transmission would berelatively large and it is suitable for a lossy compression mode with agreater degree of compression, the mode determining unit 12 selects alossy compression with a greater degree of compression as the firstcompression mode C1 for the image compression unit 14 to compress thefirst block B1, and to use a color sampling method with more sizereduction (e.g. 4:2:0) to performs color sampling. Therefore, the firstrestored block B1″ obtained after the target device 3 performsdecompression will be different from the uncompressed first block B1received by the mode determining unit 12.

In addition, if the complexity result R1 of the first block B1 output bythe classification model CM is “medium image”, indicating that thebandwidth occupied by the first block B1 during transmission would besmaller than that by the “complex image” and it is suitable for a lossycompression mode with a lesser degree of compression, the modedetermining unit 12 selects a lossy compression with a lesser degree ofcompression as the first compression mode C1 for the image compressionunit 14 to compress the first block B1, and to use a color samplingmethod with less size reduction (e.g. 4:2:2) to performs color sampling.Therefore, the first restored block B1″ obtained after the target device3 performs decompression will also be different from the uncompressedfirst block B1 received by the mode determining unit 12. As for thesecond block B2, the situations can be similarly understood, and willnot be repeated here.

As can be seen from the above, the mode determining unit 12 in the imageprocessing device 1 can select, based on the complexity result R1 and R2(e.g. complex image, medium image or simple image) of the first block B1and second block B2 of the image M, a suitable first compression mode C1and a suitable second compression mode C2 (such as lossless compressionor lossy compression with different degrees of compression) for theimage compression unit 14 to use to compress the first block B1 and thesecond block B2, and to use, corresponding to the first compression modeC1 and second compression mode C2, a complete color sampling method(e.g. 4:4:4) or a color sampling method with different size reduction(e.g. 4:2:2 or 4:2:0) to perform color sampling. This way, thetransmission not only meets the bandwidth limitation, but also preventsimage distortion perceived by human eyes.

It should be noted that although the above embodiment takes the firstblock B1 and the second block B2 of the image M as examples fordescription, in practice, the processing of other blocks of the image Mare similar and can be understood similarly, and no further details isdescribed here.

Another embodiment of the present invention is an image processingdevice. As shown in FIG. 1, in this embodiment, the image processingdevice 1 can be coupled between the image source device 2 and the targetdevice 3, but it is not limited to this. The image processing device 1may include an image capturing unit 10, a mode determining unit 12, andan image compression unit 14. The mode determining unit 12 includes aclassification model CM. The mode determining unit 12 is coupled to theimage capturing unit 10; the image compression unit 14 is coupled to theimage capturing unit 10 and the mode determining unit 12. The imagecapturing unit 10 captures the image M played by the image source device2 and divides the image M into a plurality of blocks B1 and B2. The modedetermining unit 12 receives the first block B1 of the blocks B1 and B2,and selects a first compression mode C1 for the first block B1 from theplurality of compression modes based on the output of the classificationmodel CM. The image compression unit 14 compresses the first block B1according to the first compression mode C1 to generate the firstcompressed block B1′. Similarly, the mode determining unit 12 receivesthe second block B2 of the blocks B1 and B2, and selects a secondcompression mode C2 for the second block B2 from the plurality ofcompression modes based on the output of the classification model CM.The image compression unit 14 compresses the second block B2 accordingto the second compression mode C2 to generate the second compressedblock B2′. As for the operation of each unit of the image processingdevice 1, please refer to the above related text and diagramdescription, which will not be repeated here.

Another embodiment of the present invention is an image processingmethod. In this embodiment, the image processing method can be performedby an image processing device to process the image to generate theprocessed image. The image processing system may include an imageprocessing device, an image source device, and a target device, and theimage processing device may be coupled between the image source deviceand the target device, without limitation.

Please refer to FIG. 4, which illustrates the image processing method inthis embodiment. As shown in FIG. 4, the image processing method mayinclude steps S10 to S18.

First, the image processing method captures the image provided by theimage source device (step S10) and divides the image into a plurality ofblocks (step S12), wherein the blocks include at least a first block anda second block different from each other, without limitation. Then, theimage processing method selects the first block among the blocks (stepS14), analyzes the first block, and selects, from among multiplecompression modes, a first compression mode for the first blockaccording to the analysis result (Step S16), and then compress the firstblock according to the first compression mode to generate a firstcompressed block (step S18).

More specifically, after the image processing method performs step S14to select the first block, as shown in FIG. 5, in step S16, the imageprocessing method inputs the first block to the classification model(step S160) and analyzes the block using the classification model togenerate a complexity result of the first block (step S162). Next, theimage processing method selects, based on the complexity result of thefirst block, a first compression mode for the first block from themultiple compression modes (step S164).

It should be noted that the classification model described in step S160may be an artificial neural network obtained through deep learning, forexample, by inputting a plurality of sample images to the classificationmodel to be trained, so that in the training process, the classificationmodel continuously adjusts its internal parameters based on the feedbackof the training complexity results of each sample image, therebyobtaining a trained classification model that is more accurate inprocessing new input image that have not been used as training data. Inaddition, in step S164, the complexity result of the first block can becomplex image, medium image, or simple image in terms of complexitylevel, and each corresponding to its own suitable compression mode, butit is not limited to this.

In one embodiment, after the image processing method executes step S18to compress the first block according to the first compression mode togenerate the first compressed block, as shown in FIG. 6, the imageprocessing method transmits the first compressed block to the targetdevice, where the first compressed block further includes compressioninformation corresponding to the first compression mode (step S20).Then, the target device selects a first decompression mode correspondingto the first compression mode based on the compression information (stepS22), and decompresses the first compressed block according to the firstdecompression mode to obtain the first restored block (Step S24).

It should be noted that the first compression mode may be a lossycompression or a lossless compression, and in the first compressionmode, the color sampling method may be a complete color sampling method(e.g. 4:4:4) or a reduced-size color sampling method (e.g. 4:2:2 or4:2:0), so that the first restored block obtained after decompression instep S24 may be the same as or different from the first uncompressedfirst block selected in step S14.

More specifically, if the first compression mode is lossy compressionand the color sampling is a reduced-size color sampling method (e.g.4:2:2 or 4:2:0), then the first restored block obtained afterdecompression in step S24 will be different from the uncompressed firstblock selected in step S14; and if the first compression mode is alossless compression and the color sampling is a complete color samplingmethod, then the first restored block will be the same as theuncompressed first block selected in step S14.

For example, if the complexity result of the first block is “simpleimage”, indicating that the bandwidth occupied by the first block duringtransmission would be relatively small and it is suitable to becompressed with a lossless compression mode, the image processing methodselects a lossless compression as the first compression mode to compressthe first block, and uses a complete color sampling method (e.g. 4:4:4)to perform color sampling. Therefore, the first restored block obtainedafter decompression in step S24 will be the same as the uncompressedfirst block selected in step S14.

Conversely, if the complexity result of the first block is “compleximage”, indicating that the bandwidth occupied by the first block duringtransmission would be relatively large and it is suitable to becompressed in a lossy compression mode with a greater degree ofcompression, the image processing method selects a lossy compressionwith a greater degree of compression as the first compression mode tocompress the first block, and uses a color sampling method with moresize reduction (such as 4:2:0) to perform color sampling. Therefore, thefirst restored block obtained after decompression in step S24 will bedifferent from the uncompressed first block selected in step S14.

In addition, if the complexity result of the first block is “mediumimage”, indicating that the bandwidth occupied by the transmission ofthe first block would be smaller than that by the “complex image” and itis suitable for a lossy compression mode with a lesser degree ofcompression, the image processing method selects the lossy compressionwith a lesser degree of compression as the first compression mode tocompress the first block, and uses a color sampling method with lesssize reduction (e.g. 4:2:2) to perform color sampling. Therefore, thefirst restored block obtained after decompression in step S24 will alsobe different from the uncompressed first block selected in step S14.

Similarly, as shown in FIG. 7, the image processing method may select,from the plurality of blocks, a second block that is different from thefirst block (step S30), analyze the second block, and select a secondcompression mode for the second block based on the analysis result (stepS32). Next, the image processing method compresses the second blockaccording to the second compression mode to generate a second compressedblock (step S34), and transmits the second compressed block to thetarget device, where the second compressed block also includescompression information corresponding to the second compression mode(step S36). Then, the target device selects the second decompressionmode corresponding to the second compression mode based on thecompression information (step S38), and decompresses the secondcompressed block according to the second decompression mode to obtainthe second restored block (Step S40).

It can be seen from the above that the image processing method of thepresent embodiment can select, based on the complexity results (e.g.complex image, medium image or simple image) of the first block andsecond block of the image, a suitable first compression mode and secondcompression mode (such as lossless compression or lossy compression withdifferent degrees of compression) to be used to compress the first blockand the second block, and also to use, corresponding to the firstcompression mode and second compression mode, a complete color samplingmethod (e.g. 4:4:4) or a color sampling method with different sizereduction (e.g. 4:2:2 or 4:2:0) to perform color sampling. This way, thetransmission not only meets the bandwidth limitation, but also preventsimage distortion perceived by human eyes.

It should be noted that although the above embodiment takes the firstblock and the second block of the image as examples for description, inpractice, the processing of other blocks of the image are similar andcan be understood similarly, and no further details is described here.

Compared with the conventional art, the image processing system, imageprocessing device and image processing method of embodiments of thepresent invention can use a classification model (such as an artificialneural network obtained through deep learning) to automatically processthe current image block to determine the complexity result of the imageblock (such as complex image, medium image, or simple image), and basedthereon, to select a compression mode that is the most suitable for theimage block. The technology can not only meet the bandwidth limitation,but also avoid image distortion perceived by the human eyes.

It will be apparent to those skilled in the art that variousmodification and variations can be made in the image processing system,device and method of the present invention without departing from thespirit or scope of the invention. Thus, it is intended that the presentinvention cover modifications and variations that come within the scopeof the appended claims and their equivalents.

What is claimed is:
 1. An image processing device, comprising: an imagecapturing unit, configured to capture an image and divide the image intoa plurality of blocks; a mode determining unit, having multiplecompression modes, coupled to the image capturing unit, configured toreceive a first block among the plurality of blocks, the modedetermining unit including a classification model configured to analyzethe first block, the mode determining unit further being configured to,based on an analysis result from the classification model, select afirst compression mode for the first block from the multiple compressionmodes; and an image compression unit, coupled to the image capturingunit and the mode determining unit, configured to compresses the firstblock according to the first compression mode to generate a firstcompressed block.
 2. The image processing device of claim 1, wherein theclassification model is an artificial intelligence (AI) model obtainedthrough training, wherein during training, the classification modelreceives a plurality of sample images as input and outputs complexityresults.
 3. The image processing device of claim 1, wherein the modedetermining unit is configured to input the first block to theclassification model, and to select the first compression mode for thefirst block from the multiple compression modes based on a complexityresult of the first block output by the classification model.
 4. Theimage processing device of claim 1, wherein the first compressed blockfurther includes compression information corresponding to the firstcompression mode.
 5. The image processing device of claim 1, wherein themode determining unit is further configured to obtain a second blockfrom the plurality of blocks, analyze the second block by theclassification model, and based on an analysis result, and select asecond compression mode for the second block from the multiplecompression modes, wherein the image compression unit is furtherconfigured to compress the second block according to the secondcompression mode to generate the second compressed block, and whereinthe second block is different from the first block.
 6. The imageprocessing device of claim 1, wherein the first compression mode and thesecond compression mode are different and use different color samplingmethods.
 7. An image processing system, comprising: an image sourcedevice, configured to provide an image; an image processing device,coupled to the image source device, and including: an image capturingunit, coupled to the source device, and configured to receive the imagefrom the image source device and divide the image into a plurality ofblocks; a mode determining unit, having multiple compression modes,coupled to the image capturing unit, configured to receive a first blockamong the plurality of blocks, the mode determining unit including aclassification model configured to analyze the first block, the modedetermining unit further configured to, based on an analysis result fromthe classification model, select a first compression mode for the firstblock from the multiple compression modes; and an image compressionunit, coupled to the image capturing unit and the mode determining unit,configured to compresses the first block according to the firstcompression mode to generate a first compressed block, wherein the firstcompressed block further includes compression information correspondingto the first compression mode; and a target device, coupled to the imagecompression unit of the image processing device, configured to receivethe first compressed block, to select a first decompression modecorresponding to the first compression mode based on the compressioninformation, and to decompress the first compressed block according tothe first decompression mode.
 8. The image processing system of claim 7,wherein the mode determining unit is configured to input the first blockto the classification model, and select the first compression mode forthe first block from the multiple compression modes based on acomplexity result output by the classification model.
 9. The imageprocessing system of claim 7, wherein the mode determining unit isfurther configured to obtain a second block from the plurality ofblocks, analyze the second block by the classification model, and basedon an analysis result, select a second compression mode for the secondblock from the multiple compression modes, wherein the image compressionunit is further configured to compress the second block according to thesecond compression mode to generate the second compressed block, andwherein the second block is different from the first block.
 10. Theimage processing system of claim 9, wherein the first compression modeand the second compression mode are different and use different colorsampling methods.
 11. An image processing method, comprising: capturingan image; dividing the image into a plurality of blocks; selecting afirst block among the blocks; analyzing the first block; based on ananalysis result, selecting a first compression mode for the first blockfrom a plurality of compression modes; and compressing the first blockaccording to the first compression mode to generate a first compressedblock.
 12. The image processing method of claim 11, further comprising:selecting a second block among the blocks, the second block beingdifferent from the first block; analyzing the second block; based on ananalysis result, selecting a second compression mode for the secondblock from the plurality of compression modes; and compressing thesecond block according to the second compression mode to generate asecond compressed block.
 13. The image processing method of claim 12,wherein the first compression mode and the second compression mode aredifferent and use different color sampling methods.
 14. The imageprocessing method of claim 11, wherein the first compressed blockfurther includes compression information corresponding to the firstcompression mode, the method further comprising: transmitting the firstcompressed block to a target device; the target device selecting a firstdecompression mode corresponding to the first compression mode based onthe compression information; and the target device decompressing thefirst compressed block according to the first decompression mode. 15.The image processing method of claim 11, further comprising: inputtingthe first block to a classification model; the classification modelanalyzing the block to produce a complexity result of the first block;selecting, based on the complexity result of the first block, the firstcompression mode for the first block from the multiple compressionmodes.
 16. The image processing method of claim 11, further comprising:obtaining the classification model through artificial intelligence (AI)training, wherein during training, the classification model receives aplurality of sample images and outputs complexity results.
 17. The imageprocessing method of claim 11, further comprising: under the firstcompression mode, performing sampling of the first block using either acomplete color sampling method or a reduced-size color sampling method.